refactor: move some utils to a separate file

Author: xmatthias

freqtrade/data/btanalysis/__init__.py

@@ -1,9 +1,7 @@
 # flake8: noqa: F401
 from .btanalysis import (
     BT_DATA_COLUMNS,
-    analyze_trade_parallelism,
     delete_backtest_result,
-    evaluate_result_multi,
     extract_trades_of_period,
     find_existing_backtest_stats,
     get_backtest_market_change,
@@ -27,3 +25,7 @@
     trade_list_to_dataframe,
     update_backtest_metadata,
 )
+from .trade_parallelism import (
+    analyze_trade_parallelism,
+    evaluate_result_multi,
+)

freqtrade/data/btanalysis/btanalysis.py

@@ -13,7 +13,7 @@
 import numpy as np
 import pandas as pd
 
-from freqtrade.constants import LAST_BT_RESULT_FN, IntOrInf
+from freqtrade.constants import LAST_BT_RESULT_FN
 from freqtrade.exceptions import ConfigurationError, OperationalException
 from freqtrade.ft_types import BacktestHistoryEntryType, BacktestResultType
 from freqtrade.misc import file_dump_json, json_load
@@ -491,57 +491,6 @@ def load_exit_signal_candles(backtest_dir: Path) -> dict[str, dict[str, pd.DataF
     return load_backtest_analysis_data(backtest_dir, "exited")
 
 
-def analyze_trade_parallelism(trades: pd.DataFrame, timeframe: str) -> pd.DataFrame:
-    """
-    Find overlapping trades by expanding each trade once per period it was open
-    and then counting overlaps.
-    :param trades: Trades Dataframe - can be loaded from backtest, or created
-        via trade_list_to_dataframe
-    :param timeframe: Timeframe used for backtest
-    :return: dataframe with open-counts per time-period in timeframe
-    """
-    from freqtrade.exchange import timeframe_to_resample_freq
-
-    timeframe_freq = timeframe_to_resample_freq(timeframe)
-    dates = [
-        pd.Series(
-            pd.date_range(
-                row[1]["open_date"],
-                row[1]["close_date"],
-                freq=timeframe_freq,
-                # Exclude right boundary - the date is the candle open date.
-                inclusive="left",
-            )
-        )
-        for row in trades[["open_date", "close_date"]].iterrows()
-    ]
-    deltas = [len(x) for x in dates]
-    dates = pd.Series(pd.concat(dates).values, name="date")
-    df2 = pd.DataFrame(np.repeat(trades.values, deltas, axis=0), columns=trades.columns)
-
-    df2 = pd.concat([dates, df2], axis=1)
-    df2 = df2.set_index("date")
-    df_final = df2.resample(timeframe_freq)[["pair"]].count()
-    df_final = df_final.rename({"pair": "open_trades"}, axis=1)
-    return df_final
-
-
-def evaluate_result_multi(
-    trades: pd.DataFrame, timeframe: str, max_open_trades: IntOrInf
-) -> pd.DataFrame:
-    """
-    Find overlapping trades by expanding each trade once per period it was open
-    and then counting overlaps
-    :param trades: Trades Dataframe - can be loaded from backtest, or created
-        via trade_list_to_dataframe
-    :param timeframe: Frequency used for the backtest
-    :param max_open_trades: parameter max_open_trades used during backtest run
-    :return: dataframe with open-counts per time-period in freq
-    """
-    df_final = analyze_trade_parallelism(trades, timeframe)
-    return df_final[df_final["open_trades"] > max_open_trades]
-
-
 def trade_list_to_dataframe(trades: list[Trade] | list[LocalTrade]) -> pd.DataFrame:
     """
     Convert list of Trade objects to pandas Dataframe

freqtrade/data/btanalysis/trade_parallelism.py

@@ -0,0 +1,60 @@
+import logging
+
+import numpy as np
+import pandas as pd
+
+from freqtrade.constants import IntOrInf
+
+
+logger = logging.getLogger(__name__)
+
+
+def analyze_trade_parallelism(trades: pd.DataFrame, timeframe: str) -> pd.DataFrame:
+    """
+    Find overlapping trades by expanding each trade once per period it was open
+    and then counting overlaps.
+    :param trades: Trades Dataframe - can be loaded from backtest, or created
+        via trade_list_to_dataframe
+    :param timeframe: Timeframe used for backtest
+    :return: dataframe with open-counts per time-period in timeframe
+    """
+    from freqtrade.exchange import timeframe_to_resample_freq
+
+    timeframe_freq = timeframe_to_resample_freq(timeframe)
+    dates = [
+        pd.Series(
+            pd.date_range(
+                row[1]["open_date"],
+                row[1]["close_date"],
+                freq=timeframe_freq,
+                # Exclude right boundary - the date is the candle open date.
+                inclusive="left",
+            )
+        )
+        for row in trades[["open_date", "close_date"]].iterrows()
+    ]
+    deltas = [len(x) for x in dates]
+    dates = pd.Series(pd.concat(dates).values, name="date")
+    df2 = pd.DataFrame(np.repeat(trades.values, deltas, axis=0), columns=trades.columns)
+
+    df2 = pd.concat([dates, df2], axis=1)
+    df2 = df2.set_index("date")
+    df_final = df2.resample(timeframe_freq)[["pair"]].count()
+    df_final = df_final.rename({"pair": "open_trades"}, axis=1)
+    return df_final
+
+
+def evaluate_result_multi(
+    trades: pd.DataFrame, timeframe: str, max_open_trades: IntOrInf
+) -> pd.DataFrame:
+    """
+    Find overlapping trades by expanding each trade once per period it was open
+    and then counting overlaps
+    :param trades: Trades Dataframe - can be loaded from backtest, or created
+        via trade_list_to_dataframe
+    :param timeframe: Frequency used for the backtest
+    :param max_open_trades: parameter max_open_trades used during backtest run
+    :return: dataframe with open-counts per time-period in freq
+    """
+    df_final = analyze_trade_parallelism(trades, timeframe)
+    return df_final[df_final["open_trades"] > max_open_trades]